Dental writing: AI-assisted coding for stylometric research

Evidence & verification

Source level

Peer-reviewed source
Grounded in a peer-reviewed publication. This describes the source; it does not mean the reported results have been independently reproduced.

Outcome verification

Author-reported
Outcomes are reported by the team that built the system. The entry is source-checked against the team's own publications; not independently reproduced.

Sources checked

2026-10-19 (UTC)

Source scope: Selected publisher HTML methods, AI-use statement and composite-index/causal limitations. Described study findings remain author-reported, not independently reproduced. The journal-source label follows publication identity and available journal metadata/policy, not an audit of review conduct or scientific quality.

Source-checked does not mean independently reproduced. See the Method page for how these labels are assigned.

Sources (1)

Research applicability

Disclosed AI-assisted research coding with author-retained scientific interpretation.

Getting started

Read the linked primary sources and the stated adoption requirements. Reuse suggestions are editorial and untested; no setup recipe or trial was executed.

Maintenance

Sources last checked on 2026-10-19. Future availability, maintenance, provider terms and compatibility were not verified.

License

Article rights and current licence terms were not independently audited.

Workflow & human oversight

Scoped L2 for the disclosed research-coding role: authors define the abstract corpus, features and comparisons; Claude assists Python parsing, feature computation and data organisation; authors retain scientific interpretation, conclusions and writing decisions. This role and the results are author-reported, not independently reproduced.

Workflow level is not an evidence score; higher is not better evidence.

Approach

Authors compare defined pre/post-period abstract corpora, compute vocabulary/style features and statistical summaries, and disclose Claude assistance for Python parsing, feature computation and data organisation. Authors retain interpretation. Aggregate linguistic change is observational; the unvalidated index is not an individual AI-use classifier or causal test.

Limitations

Selected primary HTML abstract, methods, AI-use statement, composite-index discussion and limitations only. The whole paper, figures, raw corpus, code, references and underlying model use were not audited. Six journals, two periods, abstract-only sampling, changing abstract length, editorial norms, language editing, demographics and possible human style adoption limit causal interpretation. The composite index lacks ground-truth validation. No individual AI-authorship detector, AI-adoption prevalence estimate, misconduct finding or clinical conclusion is established. Numerical drift does not prove authorship. Disclosed scripting assistance is not proof of code correctness or general model capability.

Reproducibility

Source text was checked only within this scope: Selected publisher HTML methods, AI-use statement and composite-index/causal limitations. No software, dataset, experiment or result was independently reproduced.

Adoption requirements

Untested suggestion: prespecify corpus, features and baselines; verify parsing against source abstracts, retain researcher interpretation and require appropriate ground truth before individual classification.

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